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Record W2558763695

Dionne Brand, Austin Clarke, and Tessa McWatt: Blackening Canada

2016· dissertation· en· W2558763695 on OpenAlexaboutno aff
Paul Barrett

Bibliographic record

VenueQSpace (Queen's University Library) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsArtArt historyVisual arts
DOInot available

Abstract

fetched live from OpenAlex

This project analyzes contemporary black diasporic writing in Canada, arguing that Dionne Brand, Austin Clarke and Tessa McWatt evince a unique form of double-consciousness in their writings.Their work transforms African-American double-consciousness by locating it simultaneously within both the black diaspora and the practice of Canadian multiculturalism.The objective of this project is to offer a critical framework for situating these writers within the legacy of both Black Atlantic and Canadian cultural production.These writers do not aim to resolve their double-consciousness but rather dwell within that contradictory doubleness and hyphenation, forcing nation and diaspora to contend with one another in a discomfiting and unsettling dialogue.These authors employ the absences of the black diaspora to imagine new forms of black cultural production, multicultural citizenship and national identity.Their works produce a grammar of diasporic double-consciousness that locates the absented origins of diaspora within Canada.Brand's depiction of temporality and Clarke's tracing of movement explore the continuities between nation and diaspora while re-membering neglected aspects of the history of black Canada, such as the life and death of Albert Johnson.McWatt extends this blackening of nation by depicting coalitions between diasporic, indigenous, raced and sexed subjects.These authors transform hegemonic Canadian narratives of nation by dwelling in the hyphen, while their evocation of memory, absence, trauma, and desire gives blackness new meaning and legitimacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.320
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.192
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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